signal-platform/docs/research/README.md

Research log — what we tested, what happened, what we kept

Every strategy question we've put to the backtest, in one table. The point is to stop re-litigating settled questions: if a row says "rejected", the experiment was run and the data said no. Detail lives in the linked docs and in reports/*.json (all committed).

The one-line summary of the whole platform: it is a long-only cross-sectional momentum book — buy the top quintile by beta-adjusted 12-1 momentum, tilt toward higher volatility, hold ≤ 10 names, cut at 1.5× ATR, then trail at 3× ATR for up to 30 trading days. After an initial stop, require the daily production gate to fail and subsequently qualify again before re-entry. Everything else in the app (composite score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is display or screening, not edge.

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1. What survived — the production strategy

Component	Status	Why it's there
Residual 12-1 momentum, top 20%, long-only	The edge. Everything else is scaffolding	Only component with a measured cross-sectional IC. Promoted July 2026
80/20 residual-momentum / 6m-volatility rank	Ranking tilt	Buys ~2pp CAGR over momentum-only; costs ~6pp drawdown
1.5× ATR initial stop	Real exit	Cuts losers fast
3× ATR trailing stop, 30-day max hold	Real exit	Best Sharpe of every exit tested
Post-stop normal gate reset	Re-entry policy	Stop always closes; a later gate failure and subsequent fresh qualification define the next signal episode. The selected study arm reached Sharpe 1.77 / CAGR 48.3% at capacity 10; live scan-before-outcome timing is stricter (Sharpe 1.68 / CAGR 44.8% analogue). Full study
Max 10 concurrent positions, 1% risk per trade	Sizing	Cap never binds in practice
Structural S/R	Human-facing product context	Clean, capped zones for charts and alerts; not read by the scanner
Gate Target Ladder	Screening machinery	Volume-free transient proposals preserve the production candidate set exactly; never an exit

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2. Rejected — do not resurrect without new data

#	Experiment	Result	Decision	Evidence
1	Gate target as a take-profit (exit at the target, with or without the trail)	Sharpe 2.04 → 1.47, CAGR halved (50.4% → 28.9%). Win rate rose (37.5% → 40.0%) — the tell: it truncates the right tail	Rejected. The target must never become an exit	sr-levels-and-exits.md · backtest-20260712-sr-target-exit.json
2	Clear-air fallback — synthesize a 3× ATR target so 52-week-high breakouts stop being vetoed by "no resistance above"	Looked strictly better in-sample (Sharpe 2.07, CAGR 62.3%, DD 20.1%) but failed a real out-of-sample holdout: Sharpe 2.78 → 2.45, higher drawdown	Rejected. Gate stays as-is	sr-levels-and-exits.md · backtest-20260712-holdout-*.json
3	Blanket S/R fallback (any missing target, not just clear air)	Sharpe 1.82, per-setup expectancy 0.583 → 0.280 R	Rejected. 65% of what it admitted were ATR/R:R filter misses, which are actively bad	sr-levels-and-exits.md
4	Expected-value gate (min_expected_value replacing the R:R + probability pair)	Structurally favoured distant lottery targets; selected worse-than-random setups	Removed June 2026. Settings dropped in migration 020	migration 009, 020
5	Blue-sky projected targets (invent a target above when none exists)	Dilutive under the ATR-trail exit	Reverted July 2026. Same root cause as #2 — better targets can't help when the exit ignores them	—
6	SPY 200d-MA regime overlay (block entries / go flat)	Halves return (315% → 138%), zero drawdown benefit	Rejected. The ATR trail already manages downside; the filter blocks the recovery entries that make the money	backtest-20260708-regime-overlay.json
7	Short setups	Fight the trend, drag expectancy	Excluded while the momentum gate is active	—
8	Standalone volatility ranking (high-vol 80, no momentum)	CAGR 31.6%, DD −34.8%, Sharpe 1.12	Rejected. Vol is a tilt, not a signal	prod-baseline
9	Low-volatility ranking	CAGR 2.7%, Sharpe 0.29	Rejected. No edge	prod-baseline
10	Inverse-vol position sizing	The apparent "win" was mis-attributed: the 20% notional cap bound on 95% of entries, so it measured concentration, not vol-sizing. Genuine inverse-vol cuts DD to −18.2% but costs ~58pp return at flat Sharpe	Rejected as edge; it's a risk-preference trade	backtest-20260709-position-sizing*.json
11	FIP path-smoothness as tie-breaker/filter	Non-monotonic within the qualified set; thinning the entry stream costs more compounding than the tilt returns	Rejected as a filter — but see §4, it's the strongest raw signal we've measured	—
12	Fixed take-profit sweep (R-multiples)	No interior optimum ever found — the best TP is "no TP"	Rejected. Momentum's edge lives in the right tail	backtest_service.py:450

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3. Tuned and confirmed — don't retest on this snapshot

A systematic single-variable sweep (July 2026) confirmed every production setting. Re-running these against the same ~4-year snapshot is wasted compute and invites overfitting.

Knob	Verdict
ATR trail multiple {1.5–4.0}	Keep 3.0 — ≤2.0 whipsaws out the right tail; ≥2.5 is a plateau
Momentum lookback (6-1, 3-1, 12-7 Novy-Marx, composites)	Keep residual 12-1 — the others have IC ≈ 0 or weaker t-stats
Selection cutoff {70…90} × book size {10, 15, 20}	Keep 80 × 10 — monotonically worse in both directions
Position sizing (equal-weight, inverse-vol, risk-% sweep)	Keep 1% fixed-fractional
Primary-target probability floor	Keep 20% — pruned lottery targets, 1,428 → 1,089 qualified, lifted Sharpe
Primary-target R:R selector	Keep 1.5 — target choice is intentionally independent of the later 2.0 activation floor
Exit policy (hold / SMA50 / 20-day low / technical-40 / ATR trail)	Keep 3× ATR trail — best Sharpe (2.04)
Activation R:R floor min_rr (swept 2026-07-12)	Keep 2.0 — best in-sample and out-of-sample. But it is a spike, not a plateau — see below
Post-stop re-entry (nine daily policy arms)	Keep normal gate reset at production capacity 10 — Sharpe 1.77 vs 1.67 immediate and 1.47 fixed cooldown 5. The result changes with book capacity; see post-stop-reentry.md

The min_rr sweep (2026-07-12)

min_rr = 2.0 had been hand-set in Admin and never swept — the gate ablation only tested the floor on vs off, never its level. Swept against portfolio Sharpe under the real exit, with a parity self-check (reproduces_production_gate: true — the row at 2.0 rebuilds production's exact 1,089-setup qualified set).

Reports: backtest-20260712-min-rr-sweep.json (in-sample), -oos.json (test window only).

min_rr	qualified	In-sample Sharpe / CAGR	OOS Sharpe / CAGR (entries ≥ 2024-07)
0.0 (floor off)	6636	1.98 / 58.5%	2.02 / 66.2%
1.2 (old code default)	3897	1.34 / 33.9%	1.12 / 28.8%
1.5	3127	1.20 / 29.6%	1.12 / 28.8%
1.75	1974	1.64 / 44.5%	1.15 / 27.4%
2.0 (live)	1089	2.04 / 50.4%	2.78 / 73.3%
2.25	577	1.64 / 31.8%	1.71 / 31.9%
2.5	286	1.67 / 29.0%	0.68 / 8.7%
3.0	89	1.09 / 9.1%	0.87 / 5.0%

Verdict: keep 2.0. It is the optimum in both windows, and the peak reproducing in data it was never fitted to is real evidence — the one thing the clear-air experiment couldn't show.

But treat it as fragile, and do not nudge it. Unlike the ATR trail (a plateau above 2.5), this is a spike with a trough beside it: ±0.25 costs ~0.4 Sharpe in-sample and ~1.6 Sharpe out-of-sample. A knob that sharp is not a robustly identified parameter, and the curve is bimodal (floor-off is good, 1.2–1.75 is bad, 2.0 is good) — which is not how a well-behaved threshold behaves. We got lucky: the hand-set value landed on the peak.

Also worth knowing: turning the floor off entirely is the second-best row in both windows — nearly the same Sharpe with substantially higher CAGR (58.5% / 66.2%) and more trades. If CAGR ever matters more than Sharpe here, "no R:R floor" is a live option, and it would also sever the last dependency the gate has on the weak S/R detector.

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4. Phase A matrix (2026-07-18) — closed

Full write-up: phase-a-matrix.md · reports/research-matrix-phase-a.json.

Arm	Decision
Max-hold {45,60,90}	Note and move on — validation glitter, train collapse (regime interaction)
Equity-curve vol targeting	Reject as edge on this sample; park vt25 as optional DD insurance only
Correlation caps	Reject; sector caps stay Phase B with reduced expectations
Next-open fill	Discovery, not reject — honest deployable ~Sharpe 1.2 / CAGR 30%. Decision baseline for future promotion = next_open
fip_id re-derive	Validated (IC −0.045, t = −2.92)

Highest-leverage open work: near-close execution recovery (scheduling, not a new signal). Simulator: scripts/run_execution_recovery_matrix.py (stale_close + gap-cap).

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5. Open leads

Lead	Why it's interesting	Blocker
Near-close / MOC execution	Recovers the overnight momentum drift a 07:00-Berlin scanner leaves on the table (~0.5 Sharpe / ~18pp CAGR vs close-fill)	Prove with stale_close arm; then schedule change
fip_id (information discreteness over the 12-1 window)	Strongest cross-sectional signal measured on this universe — IC −0.045, t = −2.91, correct sign; re-derived fingerprint matched Phase A	Doesn't improve this book. Revisit when the universe broadens
Broader universe (nasdaq_all)	Strengthens every week's cross-section and the IC t-stat	Also where fip_id could become tradeable
Forward paper-trade record	The only true out-of-sample evidence the snapshot cannot give	Time
Better target model for clear-air names	The return is demonstrably there (#2 wins on raw CAGR in both train and test); it's the flat 3× ATR target that makes it too expensive in risk	Needs a per-name model, not a constant k×ATR

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6. Method rules learned the hard way

1. Nested lookback windows are NOT out-of-sample. The clear-air result (#2) was clean, large, and consistent across five nested windows — and still died on a proper train/test split by entry date. Use BACKTEST_HOLDOUT_SPLIT.
2. Check what population an ablation actually admits. The blanket fallback (#3) looked like it tested the "resistance famine" hypothesis. It didn't — 65% of the setups it let in were a different population entirely, and they drove the result.
3. A rising win rate is a warning, not a win. Both #1 and #12 raised the hit rate while destroying returns. In a right-tailed strategy, "winning more often" usually means you clipped the winners.
4. The iron rule: a signal earns its way into selection only through the factor harness — |mean IC| ≳ 0.03, consistent sign, reliable: true (≥ 12 non-overlapping windows). Never let an unvalidated score gate setups.

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7. Why we stay with the current strategy

Everything we've tried to add has either failed the backtest, failed out-of-sample, or turned out to be measuring something other than what it claimed. What's left is a boring, well-documented result: cross-sectional momentum works; the machinery around it mostly doesn't.

Structural S/R, the composite score, sentiment and fundamentals remain useful human context but have no measured edge. The Gate Target Ladder is different: it is internal screening machinery whose broad historical-price-traffic behavior was preserved explicitly and volume-free, with exact full-period parity. It is still neither market structure nor an exit. The one component that does have measured predictive edge is the momentum gate, and every knob on it has been swept and confirmed. After an initial-stop exit, that same gate now also defines when a new episode may begin: one later failed observation followed by a fresh qualification. The daily re-entry matrix supports this for the current 10-position book, but not as a universal rule for other portfolio capacities.

The next real evidence is forward, not backward: the live paper-trade record.